{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3489f91-3cdd-4b6c-8ccd-1082ba608ba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (128 CPUs, 1007.5 GB RAM, 24.0/30.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "import comet_ml\n",
    "import torch\n",
    "import utils\n",
    "\n",
    "comet_ml.init(project_name='exp_100epoch')\n",
    "# 这里应该会包含100epoch的0,0.6,1.2加雾以及各个以100epoch为单位的增量\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c2082ac-c341-480f-9fd5-bb206e706cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.2':0,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a8f55c3-8ae7-48dd-a861-f7ebcf844189",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=yolov5s.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 15b6559f Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/d81ea681017f4317909b167d7a3af48f\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_02/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_02\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.71G    0.08072    0.04759    0.03296        128        640: 1\n",
      "tensor([1.75600], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.734      0.129      0.124     0.0419\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.71G    0.06457       0.04    0.02248        133        640: 1\n",
      "tensor([1.65806], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.204       0.35      0.215     0.0908\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.71G    0.05972    0.03868    0.01937        131        640: 1\n",
      "tensor([1.42281], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.536      0.337      0.306      0.138\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.71G    0.05331    0.03761    0.01617        108        640: 1\n",
      "tensor([1.15876], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.671      0.424       0.44      0.227\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.71G    0.04864    0.03638    0.01399        156        640: 1\n",
      "tensor([1.21092], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.709      0.473      0.524      0.264\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.71G    0.04601     0.0358    0.01273        123        640: 1\n",
      "tensor([1.12633], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.579      0.589      0.581      0.298\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.71G    0.04408     0.0351    0.01141        174        640: 1\n",
      "tensor([1.29816], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.698        0.6       0.62      0.328\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.71G     0.0432    0.03418    0.01049        166        640: 1\n",
      "tensor([1.34341], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.643      0.612      0.652      0.352\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.71G    0.04215    0.03428   0.009365        152        640: 1\n",
      "tensor([1.09411], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.751      0.619      0.698       0.39\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.71G    0.04123    0.03388   0.008728        136        640: 1\n",
      "tensor([1.01852], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.726      0.665      0.721      0.396\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.71G     0.0405    0.03344    0.00792        134        640: 1\n",
      "tensor([1.05314], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.767      0.669      0.742      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.71G    0.03981    0.03271   0.007507        182        640: 1\n",
      "tensor([1.09295], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.803      0.668       0.74      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.71G    0.03912    0.03299   0.007183        128        640: 1\n",
      "tensor([0.91446], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.769      0.685      0.758       0.44\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.71G    0.03848     0.0321   0.006699        112        640: 1\n",
      "tensor([0.96948], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.854      0.688      0.774       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.71G    0.03776    0.03205   0.006439        151        640: 1\n",
      "tensor([0.94161], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.851      0.684      0.773      0.461\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.71G    0.03732    0.03199   0.006223        132        640: 1\n",
      "tensor([0.97841], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.698      0.785      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.71G    0.03695    0.03138   0.006012        131        640: 1\n",
      "tensor([0.88883], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.798      0.727      0.788      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.71G    0.03644    0.03126    0.00583        159        640: 1\n",
      "tensor([1.05659], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.817      0.737      0.804      0.481\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.71G    0.03595    0.03092   0.005616        125        640: 1\n",
      "tensor([0.84348], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.818      0.709      0.786      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.71G    0.03575    0.03108   0.005572         88        640: 1\n",
      "tensor([0.74057], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.748      0.815      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.71G     0.0354    0.03007   0.005236        137        640: 1\n",
      "tensor([1.05580], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.753       0.82      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.71G    0.03491    0.03062   0.005055        166        640: 1\n",
      "tensor([0.94961], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.752      0.812      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.71G    0.03422    0.03006   0.004975        161        640: 1\n",
      "tensor([0.97848], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.838      0.748       0.83      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.71G    0.03455    0.02969   0.004878        118        640: 1\n",
      "tensor([0.80235], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.735      0.834      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.71G    0.03398    0.02962   0.004707        151        640: 1\n",
      "tensor([0.92674], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.751       0.83      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.71G    0.03386    0.02971    0.00453        133        640: 1\n",
      "tensor([0.87556], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.787      0.846      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.71G    0.03351    0.02934   0.004368        154        640: 1\n",
      "tensor([0.97849], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874       0.77      0.837      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.71G    0.03322    0.02931   0.004458        122        640: 1\n",
      "tensor([0.80563], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.761      0.836      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.71G    0.03283    0.02908   0.004303        123        640: 1\n",
      "tensor([0.73215], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.762      0.852      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.71G    0.03309    0.02876   0.004284        127        640: 1\n",
      "tensor([0.77846], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.784      0.838      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.71G    0.03233    0.02775   0.004116        127        640: 1\n",
      "tensor([0.74355], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.777      0.853      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.71G    0.03234    0.02826   0.004087        122        640: 1\n",
      "tensor([0.83938], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.795      0.861      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.71G    0.03221    0.02851   0.004088        146        640: 1\n",
      "tensor([0.90675], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.745      0.843      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.71G    0.03181    0.02782   0.003938        202        640: 1\n",
      "tensor([0.95553], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.771      0.853      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.71G    0.03183    0.02765   0.003816         94        640: 1\n",
      "tensor([0.67400], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.772      0.855      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.71G     0.0316    0.02764   0.003761        152        640: 1\n",
      "tensor([0.89800], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.803      0.858      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.71G    0.03133    0.02744   0.003756        123        640: 1\n",
      "tensor([0.74934], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898       0.78      0.868      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.71G    0.03142    0.02757   0.003735        162        640: 1\n",
      "tensor([0.80210], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888        0.8      0.858      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.71G    0.03102    0.02765   0.003638        161        640: 1\n",
      "tensor([0.85241], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.815      0.869       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.71G    0.03094    0.02754   0.003608        122        640: 1\n",
      "tensor([0.72625], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.803       0.86      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.71G    0.03072    0.02703   0.003622        126        640: 1\n",
      "tensor([0.73243], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.786      0.865      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.71G    0.03042    0.02696   0.003513         90        640: 1\n",
      "tensor([0.67693], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.789      0.873      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.71G    0.03027     0.0268   0.003337        118        640: 1\n",
      "tensor([0.77790], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.817      0.877      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.71G    0.03025    0.02679   0.003301        157        640: 1\n",
      "tensor([0.80486], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.784      0.869      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.71G    0.03017     0.0268   0.003329        104        640: 1\n",
      "tensor([0.58417], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.807      0.864      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.71G    0.03004    0.02709   0.003234        157        640: 1\n",
      "tensor([0.77997], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.799      0.869      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.71G    0.03001    0.02643   0.003222        108        640: 1\n",
      "tensor([0.59330], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.819      0.875      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.71G    0.02973    0.02634   0.003287        159        640: 1\n",
      "tensor([0.82862], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.804      0.861      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.71G    0.02938    0.02615   0.003199        118        640: 1\n",
      "tensor([0.71773], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.791      0.863      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.71G    0.02936    0.02654   0.003175        176        640: 1\n",
      "tensor([0.89009], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.782      0.866      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.71G    0.02918    0.02597   0.003111        130        640: 1\n",
      "tensor([0.69951], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895       0.81       0.87      0.593\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.71G    0.02893    0.02618   0.003081        178        640: 1\n",
      "tensor([0.90317], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.806      0.861      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.71G    0.02883    0.02573   0.003088        148        640: 1\n",
      "tensor([0.75652], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.807      0.868      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.71G    0.02873    0.02562   0.003017        115        640: 1\n",
      "tensor([0.67199], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.804      0.874      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.71G    0.02863    0.02555   0.002981        124        640: 1\n",
      "tensor([0.67098], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.816      0.881      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.71G    0.02847    0.02507   0.002964        163        640: 1\n",
      "tensor([0.75758], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887       0.82      0.875        0.6\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.71G    0.02812    0.02553   0.002928        200        640: 1\n",
      "tensor([0.83031], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.803      0.875      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.71G    0.02827    0.02545   0.003022        141        640: 1\n",
      "tensor([0.71872], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.823      0.877        0.6\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.71G    0.02822    0.02538   0.003048        146        640: 1\n",
      "tensor([0.71913], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.814      0.879      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.71G    0.02794    0.02513   0.002894        168        640: 1\n",
      "tensor([0.73726], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.829      0.875      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.71G    0.02783    0.02479   0.002749        175        640: 1\n",
      "tensor([0.78288], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.817      0.891      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.71G    0.02752    0.02494   0.002823        139        640: 1\n",
      "tensor([0.79616], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.799      0.884      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.71G    0.02734    0.02406   0.002757        117        640: 1\n",
      "tensor([0.64282], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891       0.82      0.882      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.71G    0.02757    0.02453   0.002707        129        640: 1\n",
      "tensor([0.67578], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.803      0.876      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.71G     0.0272    0.02457   0.002829        109        640: 1\n",
      "tensor([0.63226], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.814      0.891      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.71G    0.02724    0.02473   0.002661        154        640: 1\n",
      "tensor([0.79511], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.812      0.882       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.71G    0.02693    0.02447   0.002679        119        640: 1\n",
      "tensor([0.67969], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.811      0.883      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.71G    0.02695    0.02389   0.002731        153        640: 1\n",
      "tensor([0.72823], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.812      0.885      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.71G     0.0268    0.02431    0.00268        116        640: 1\n",
      "tensor([0.64042], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.822      0.878      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.71G    0.02647    0.02364   0.002644        141        640: 1\n",
      "tensor([0.72498], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.815      0.881      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.71G    0.02654    0.02393   0.002527        175        640: 1\n",
      "tensor([0.84789], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.817      0.876      0.618\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.71G     0.0266    0.02394   0.002583        161        640: 1\n",
      "tensor([0.72434], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918       0.82      0.881      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.71G    0.02638    0.02363   0.002459        114        640: 1\n",
      "tensor([0.60553], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.821      0.881       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.71G    0.02654    0.02402   0.002604        141        640: 1\n",
      "tensor([0.73110], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911       0.82      0.882      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.71G    0.02632    0.02387   0.002496        133        640: 1\n",
      "tensor([0.60088], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.935      0.807      0.886      0.625\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.71G    0.02597    0.02335   0.002408        159        640: 1\n",
      "tensor([0.75045], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.815      0.883      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.71G    0.02613    0.02336   0.002461        122        640: 1\n",
      "tensor([0.61219], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.824      0.884       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.71G    0.02605    0.02365   0.002454        137        640: 1\n",
      "tensor([0.69006], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.829       0.88      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.71G    0.02595    0.02313   0.002439        137        640: 1\n",
      "tensor([0.66274], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.935      0.816      0.879      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.71G    0.02544     0.0227   0.002431        161        640: 1\n",
      "tensor([0.77197], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.939      0.826      0.884      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.71G    0.02558    0.02317   0.002364        154        640: 1\n",
      "tensor([0.61898], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.832      0.888      0.634\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.71G    0.02543    0.02284   0.002342        181        640: 1\n",
      "tensor([0.78805], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.819      0.888       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.71G    0.02531    0.02273   0.002352        149        640: 1\n",
      "tensor([0.65280], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.816       0.89      0.634\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.71G    0.02519     0.0228   0.002253        118        640: 1\n",
      "tensor([0.59273], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.935      0.825      0.893      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.71G    0.02503    0.02252   0.002272        178        640: 1\n",
      "tensor([0.77202], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.836       0.89      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.71G    0.02495    0.02258   0.002223        140        640: 1\n",
      "tensor([0.67576], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.835      0.883      0.634\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.71G     0.0251    0.02269    0.00232        119        640: 1\n",
      "tensor([0.55480], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.834      0.887      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.71G    0.02494    0.02275   0.002232        114        640: 1\n",
      "tensor([0.52315], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.832      0.888      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.71G    0.02504    0.02246    0.00231        117        640: 1\n",
      "tensor([0.56983], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.825      0.891      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.71G    0.02486    0.02247    0.00219        118        640: 1\n",
      "tensor([0.55603], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.832      0.884      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.71G    0.02471    0.02213   0.002115        115        640: 1\n",
      "tensor([0.60222], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.831      0.884      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.71G     0.0245    0.02188   0.002164        159        640: 1\n",
      "tensor([0.70914], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.831       0.89      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.71G    0.02446    0.02212   0.002219        165        640: 1\n",
      "tensor([0.69678], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.834      0.888      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.71G     0.0244    0.02186   0.002188        126        640: 1\n",
      "tensor([0.61517], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.838      0.889      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.71G    0.02427    0.02186   0.002212        112        640: 1\n",
      "tensor([0.57639], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.834      0.889      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.71G     0.0241    0.02164   0.002035        121        640: 1\n",
      "tensor([0.58734], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.834      0.889      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.71G    0.02437    0.02159   0.002182        195        640: 1\n",
      "tensor([0.64685], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.839       0.89       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.71G    0.02427    0.02166   0.002084        101        640: 1\n",
      "tensor([0.57606], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.831      0.887      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.71G    0.02399    0.02151    0.00213        137        640: 1\n",
      "tensor([0.54862], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.937      0.832      0.888      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.71G    0.02401    0.02167   0.002066        115        640: 1\n",
      "tensor([0.51317], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.94      0.831      0.889      0.648\n",
      "\n",
      "100 epochs completed in 1.175 hours.\n",
      "Optimizer stripped from runs/train/fog_02/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_02/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_02/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.838       0.89       0.65\n",
      "                   Car       1048       4012      0.951      0.914      0.968      0.785\n",
      "                   Van       1048        431      0.961      0.922      0.967      0.792\n",
      "                 Truck       1048        166      0.957      0.948       0.97      0.792\n",
      "                  Tram       1048         56       0.93      0.964      0.962      0.744\n",
      "            Pedestrian       1048        618      0.897       0.72      0.837       0.46\n",
      "        Person_sitting       1048         20      0.984        0.6      0.639        0.4\n",
      "               Cyclist       1048        234      0.916      0.837      0.893      0.594\n",
      "                  Misc       1048        138      0.894      0.797      0.886      0.632\n",
      "Results saved to \u001b[1mruns/train/fog_02\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/d81ea681017f4317909b167d7a3af48f\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9318673007063577\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 190.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9677518482333825\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7851211596385506\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9507081290278193\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9137587238285144\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3666.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8747895123926656\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 18.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8925113182050032\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5936735363895767\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.915854766579595\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.8372488058478397\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 196.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8429639098562679\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.886493924151313\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.632493067644817\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8944260938865396\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.7971014492753623\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 110.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7989621860651147\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8367411926474866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4601856230778494\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8971849782702804\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7201238455283764\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 445.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7453630964697562\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.6392829816557362\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.40003769841269843\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9836813826797133\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9467703054896351\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9615583056104959\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7442427363694567\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.9298798492182196\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9642857142857143\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 54.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9526419631028669\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 7.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9696580169147813\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7924914311986386\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.957408929515577\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9479222312319406\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 157.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.941240147065405\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 16.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9668058023609265\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7919628348025582\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9612950260620776\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9220049515873182\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 397.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5238125920295715, 3.8253629207611084)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.12440170950032978, 0.893323561842426)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.041863648320530356, 0.6499205160179643)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.20411936276719356, 0.9401690714376074)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.12897303924273582, 0.8385422046197091)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023988151922822, 0.08072442561388016)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.002035344485193491, 0.03295828402042389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.021509764716029167, 0.047586824744939804)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.02495572343468666, 0.06580623239278793)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.002800025511533022, 0.02470795437693596)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03877443075180054, 0.059059951454401016)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/d81ea681017f4317909b167d7a3af48f\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_02\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : yolov5s.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.79 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "这是没有加雾的，原始的100epoch训练的baseline。\n",
    "SI_enable单独启用表示开启记录si。不添加SI_pt则不会计算SI损失\n",
    "'''\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--name fog_0 \\\n",
    "--SI_enable \\\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45331c81-052b-415c-8779-6d5d552cf2b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "621876d4-cdd7-46db-a76b-d4b310dffc85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b877b41-b31d-4363-92cf-51911160794d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "接下来训练0.6强度的baseline。我们的增量训练是基于不加雾的baseline的，所以0.6强度的baseline不需要计算SI。\n",
    "\n",
    "'''\n",
    "val_fogged_strength = 0.6\n",
    "# 替换验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fcfb8ea3-6a60-4855-a2eb-d0a46cb7238d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 替换训练数据\n",
    "origin_ratio = {\n",
    "    '0.6':1,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79f952ef-0adb-4488-9de0-024e2e195058",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=yolov5s.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0.6, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 15b6559f Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/66887b7d3b82416ea5b774ae44a2c57b\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val... 1048 images, 0 backg\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0.6/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0.6\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.65G    0.08099    0.04804    0.03313        128        640: 1\n",
      "tensor([1.77082], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.739      0.121      0.117     0.0412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.65G    0.06496    0.04032    0.02256        133        640: 1\n",
      "tensor([1.68924], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.278      0.332      0.204     0.0851\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.65G    0.05983      0.039    0.01955        131        640: 1\n",
      "tensor([1.40925], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.46      0.386      0.277      0.127\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.65G    0.05352    0.03793    0.01629        108        640: 1\n",
      "tensor([1.19524], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.671       0.38      0.398      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.65G    0.04883    0.03668    0.01418        156        640: 1\n",
      "tensor([1.20303], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.693       0.46      0.518      0.255\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.65G    0.04617     0.0361    0.01295        123        640: 1\n",
      "tensor([1.12881], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.757      0.459      0.534      0.265\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.65G    0.04427     0.0353     0.0117        174        640: 1\n",
      "tensor([1.28648], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.727      0.498      0.548      0.293\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.65G    0.04342    0.03446    0.01071        166        640: 1\n",
      "tensor([1.36306], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.668      0.547      0.591      0.317\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.65G     0.0422    0.03456   0.009617        152        640: 1\n",
      "tensor([1.15085], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.722      0.586      0.649      0.355\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.65G    0.04148    0.03412   0.009143        136        640: 1\n",
      "tensor([1.06756], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.662      0.542       0.58       0.32\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.65G    0.04077    0.03378   0.008294        134        640: 1\n",
      "tensor([1.11647], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.777      0.621      0.693      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.65G     0.0399      0.033   0.007808        182        640: 1\n",
      "tensor([1.11631], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.798      0.625      0.724      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.65G    0.03937    0.03326   0.007372        128        640: 1\n",
      "tensor([0.91988], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.811       0.65       0.74      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.65G    0.03847    0.03246   0.006919        112        640: 1\n",
      "tensor([1.02482], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.786      0.694      0.765      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.65G    0.03795    0.03237   0.006574        151        640: 1\n",
      "tensor([0.95470], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.757      0.673      0.741      0.437\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.65G    0.03754    0.03228   0.006357        132        640: 1\n",
      "tensor([0.96284], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.824      0.705      0.782      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.65G    0.03688    0.03155   0.006103        131        640: 1\n",
      "tensor([0.88573], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.757      0.688      0.749      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.65G    0.03707    0.03159   0.005937        159        640: 1\n",
      "tensor([1.03401], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.785      0.746      0.799      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.65G    0.03628    0.03108    0.00566        125        640: 1\n",
      "tensor([0.83041], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.646       0.75      0.454\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.65G    0.03607    0.03123   0.005602         88        640: 1\n",
      "tensor([0.72816], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.736      0.817      0.496\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.65G    0.03507    0.03016   0.005342        137        640: 1\n",
      "tensor([1.06116], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.708       0.81      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.65G    0.03507    0.03078   0.005227        166        640: 1\n",
      "tensor([0.96004], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.723      0.825      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.65G    0.03497    0.03037    0.00513        161        640: 1\n",
      "tensor([1.00034], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.854      0.741      0.815      0.496\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.65G    0.03477    0.02991   0.004964        118        640: 1\n",
      "tensor([0.83150], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866       0.75      0.828      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.65G     0.0339    0.02984   0.004803        151        640: 1\n",
      "tensor([0.99059], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.759      0.833       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.65G    0.03409     0.0299   0.004747        133        640: 1\n",
      "tensor([0.86987], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.748      0.826      0.516\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.65G    0.03387    0.02967   0.004592        154        640: 1\n",
      "tensor([1.00072], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.757      0.839      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.65G    0.03337    0.02957   0.004618        122        640: 1\n",
      "tensor([0.79220], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.769      0.843      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.65G    0.03331    0.02942   0.004349        123        640: 1\n",
      "tensor([0.76648], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.774      0.847      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.65G     0.0332    0.02902   0.004321        127        640: 1\n",
      "tensor([0.77439], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.751      0.838      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.65G    0.03265    0.02801   0.004223        127        640: 1\n",
      "tensor([0.74959], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.758      0.848      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.65G    0.03279    0.02859   0.004254        122        640: 1\n",
      "tensor([0.85831], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.785       0.85       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.65G    0.03253    0.02866   0.004078        146        640: 1\n",
      "tensor([0.88772], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.771      0.858      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.65G    0.03228    0.02807   0.003948        202        640: 1\n",
      "tensor([0.95456], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.836      0.793      0.844      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.65G    0.03202     0.0279     0.0039         94        640: 1\n",
      "tensor([0.68287], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.788      0.859       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.65G    0.03186    0.02791   0.003896        152        640: 1\n",
      "tensor([0.92625], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.801      0.863      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.65G    0.03154    0.02771   0.003823        123        640: 1\n",
      "tensor([0.73747], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.781       0.87      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.65G    0.03151    0.02776   0.003826        162        640: 1\n",
      "tensor([0.81226], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.791      0.859      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.65G    0.03122    0.02785    0.00373        161        640: 1\n",
      "tensor([0.82944], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.802      0.864      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.65G    0.03104     0.0277    0.00359        122        640: 1\n",
      "tensor([0.73886], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.807      0.868      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.65G    0.03091    0.02724    0.00377        126        640: 1\n",
      "tensor([0.71674], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.797      0.868      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.65G    0.03066    0.02726   0.003592         90        640: 1\n",
      "tensor([0.67541], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.811      0.872      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.65G     0.0304    0.02702   0.003495        118        640: 1\n",
      "tensor([0.81175], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.791      0.867      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.65G    0.03052    0.02709   0.003322        157        640: 1\n",
      "tensor([0.80621], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.784      0.874      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.65G    0.03033    0.02696    0.00334        104        640: 1\n",
      "tensor([0.59499], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.791      0.876      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.65G    0.03032    0.02738   0.003267        157        640: 1\n",
      "tensor([0.79632], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.802      0.872      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.65G       0.03    0.02658   0.003215        108        640: 1\n",
      "tensor([0.60224], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.813      0.877      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.65G    0.02984    0.02641   0.003339        159        640: 1\n",
      "tensor([0.81110], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.802       0.88      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.65G    0.02945     0.0263   0.003184        118        640: 1\n",
      "tensor([0.70542], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.804       0.88      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.65G    0.02973    0.02666   0.003184        176        640: 1\n",
      "tensor([0.90083], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.806      0.876      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.65G    0.02935    0.02621   0.003218        130        640: 1\n",
      "tensor([0.74748], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.805      0.876      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.65G    0.02924    0.02639   0.003048        178        640: 1\n",
      "tensor([0.91178], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903       0.81      0.877      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.65G    0.02913    0.02598   0.003086        148        640: 1\n",
      "tensor([0.77168], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.813      0.884      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.65G    0.02891    0.02587   0.003023        115        640: 1\n",
      "tensor([0.71590], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.821      0.877      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.65G    0.02875    0.02566   0.002981        124        640: 1\n",
      "tensor([0.66760], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.819      0.877      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.65G    0.02869    0.02532   0.003043        163        640: 1\n",
      "tensor([0.76423], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.814      0.877      0.602\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.65G    0.02837    0.02572   0.002977        200        640: 1\n",
      "tensor([0.82381], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.825      0.871      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.65G    0.02842    0.02558   0.002974        141        640: 1\n",
      "tensor([0.71731], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.813      0.882      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.65G    0.02848     0.0255   0.003022        146        640: 1\n",
      "tensor([0.72214], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.826      0.885      0.605\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.65G    0.02803    0.02532   0.002894        168        640: 1\n",
      "tensor([0.75556], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.824      0.879      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.65G    0.02799    0.02501   0.002743        175        640: 1\n",
      "tensor([0.77768], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.831      0.887      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.65G    0.02769    0.02516   0.002876        139        640: 1\n",
      "tensor([0.79714], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.814       0.88      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.65G    0.02756    0.02427   0.002829        117        640: 1\n",
      "tensor([0.65763], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.825      0.882      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.65G    0.02793    0.02472   0.002685        129        640: 1\n",
      "tensor([0.71931], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.816      0.888      0.611\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.65G    0.02751    0.02477   0.002794        109        640: 1\n",
      "tensor([0.62416], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.795      0.885      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.65G    0.02744    0.02488   0.002714        154        640: 1\n",
      "tensor([0.76416], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.838      0.892      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.65G    0.02717    0.02461   0.002709        119        640: 1\n",
      "tensor([0.66720], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.819      0.884      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.65G    0.02701    0.02404   0.002711        153        640: 1\n",
      "tensor([0.74266], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.833       0.89      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.65G    0.02699     0.0245   0.002671        116        640: 1\n",
      "tensor([0.62054], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.833      0.889      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.65G    0.02668    0.02376   0.002688        141        640: 1\n",
      "tensor([0.70365], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907       0.84      0.894      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.65G    0.02667    0.02409   0.002584        175        640: 1\n",
      "tensor([0.86689], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.835      0.893      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.65G    0.02677    0.02403   0.002593        161        640: 1\n",
      "tensor([0.73399], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.817      0.889      0.621\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.65G    0.02654    0.02375   0.002487        114        640: 1\n",
      "tensor([0.62394], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.834      0.894      0.622\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.65G    0.02662    0.02415   0.002607        141        640: 1\n",
      "tensor([0.72855], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.837      0.893      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.65G    0.02648    0.02404   0.002465        133        640: 1\n",
      "tensor([0.60075], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.822      0.893      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.65G    0.02618    0.02349   0.002425        159        640: 1\n",
      "tensor([0.76792], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.826       0.89       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.65G    0.02637    0.02358   0.002541        122        640: 1\n",
      "tensor([0.61781], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.822      0.888      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.65G    0.02614    0.02372   0.002482        137        640: 1\n",
      "tensor([0.67765], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.827      0.888      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.65G    0.02616    0.02333   0.002477        137        640: 1\n",
      "tensor([0.68365], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912       0.83      0.892      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.65G    0.02561    0.02287   0.002483        161        640: 1\n",
      "tensor([0.75689], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.933      0.845      0.894      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.65G    0.02579    0.02334   0.002377        154        640: 1\n",
      "tensor([0.64916], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.832      0.892      0.634\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.65G    0.02559    0.02298   0.002414        181        640: 1\n",
      "tensor([0.77396], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927       0.84        0.9       0.64\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.65G    0.02549    0.02291   0.002339        149        640: 1\n",
      "tensor([0.66549], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.829      0.889      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.65G    0.02542    0.02297   0.002281        118        640: 1\n",
      "tensor([0.61051], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.853      0.896      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.65G    0.02518    0.02274   0.002296        178        640: 1\n",
      "tensor([0.78310], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927       0.84      0.892      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.65G    0.02514    0.02271   0.002267        140        640: 1\n",
      "tensor([0.67968], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.829      0.888      0.636\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.65G    0.02524    0.02281   0.002376        119        640: 1\n",
      "tensor([0.56388], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.836      0.891      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.65G    0.02508    0.02288   0.002236        114        640: 1\n",
      "tensor([0.52956], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.838      0.893      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.65G     0.0252    0.02252   0.002276        117        640: 1\n",
      "tensor([0.56921], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.839      0.894      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.65G    0.02501    0.02263   0.002204        118        640: 1\n",
      "tensor([0.56906], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.848      0.897      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.65G    0.02487    0.02228   0.002133        115        640: 1\n",
      "tensor([0.60212], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.837      0.894      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.65G    0.02464    0.02201   0.002135        159        640: 1\n",
      "tensor([0.71067], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.842      0.892      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.65G    0.02462    0.02221   0.002216        165        640: 1\n",
      "tensor([0.70865], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.841      0.893      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.65G    0.02453    0.02197   0.002206        126        640: 1\n",
      "tensor([0.59431], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.836      0.893      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.65G    0.02444    0.02197   0.002203        112        640: 1\n",
      "tensor([0.59481], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925       0.84      0.894      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.65G    0.02427    0.02181    0.00206        121        640: 1\n",
      "tensor([0.58485], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.846      0.897      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.65G    0.02452    0.02172   0.002183        195        640: 1\n",
      "tensor([0.64296], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.846      0.898      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.65G    0.02439    0.02182   0.002108        101        640: 1\n",
      "tensor([0.58132], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.843      0.899      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.65G    0.02414    0.02166   0.002133        137        640: 1\n",
      "tensor([0.54634], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.846      0.898      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.65G     0.0242     0.0218   0.002052        115        640: 1\n",
      "tensor([0.50542], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.935      0.842      0.897      0.645\n",
      "\n",
      "100 epochs completed in 1.041 hours.\n",
      "Optimizer stripped from runs/train/fog_0.6/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0.6/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0.6/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.847      0.898      0.647\n",
      "                   Car       1048       4012       0.95      0.915      0.966      0.778\n",
      "                   Van       1048        431      0.949       0.93       0.97      0.785\n",
      "                 Truck       1048        166      0.956      0.934      0.971      0.785\n",
      "                  Tram       1048         56      0.899      0.982      0.966      0.733\n",
      "            Pedestrian       1048        618      0.896      0.712       0.83      0.465\n",
      "        Person_sitting       1048         20       0.98       0.65      0.706      0.413\n",
      "               Cyclist       1048        234      0.899      0.795       0.89      0.582\n",
      "                  Misc       1048        138      0.893      0.855      0.885      0.636\n",
      "Results saved to \u001b[1mruns/train/fog_0.6\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/66887b7d3b82416ea5b774ae44a2c57b\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9319196968681314\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 194.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9659005013354112\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7784460923885377\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9497905213037066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9147089512244049\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3670.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8437797593024493\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 21.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8904053991736518\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5821508984575998\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8991008737737315\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.7948717948717948\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 186.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8737281909509833\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 14.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8854370314034807\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.6364736464405352\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.893216125672266\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.855072463768116\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 118.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7935810565723671\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8298485225674928\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.46525668911102364\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8963175736524588\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7119741100323624\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 440.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7816507063425565\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7059614037106896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4125564071823328\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9801748846568832\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.65\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.938557154202999\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 6.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9661864435733852\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7331052902563807\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.8986755764570585\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9821428571428571\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 55.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9449018061268554\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 7.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9714214000364637\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7848400278648828\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9563390024748101\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9337349397590361\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 155.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9397870416561783\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 21.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9699048384127691\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7848051328456946\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9493712281965898\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9303944315545244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 401.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.532261848449707, 3.8057942390441895)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.11698274015635535, 0.899544802979639)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.041222912640017234, 0.6472977031659128)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.2779253740771476, 0.9356274765226559)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.12117115039281656, 0.8526629037289057)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.02413942851126194, 0.08098980784416199)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0020517169032245874, 0.0331270657479763)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.021662749350070953, 0.04804159328341484)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.025161616504192352, 0.06530559808015823)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0028285549487918615, 0.02475030906498432)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03917098790407181, 0.06063958257436752)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/66887b7d3b82416ea5b774ae44a2c57b\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : yolov5s.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.78 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "100epoch 的0.6\n",
    "不需要计算SI。\n",
    "'''\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--name fog_0.6 \\\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d926cb93-604e-493c-9bb2-3485c253d598",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f910286-142c-42a2-a1ec-62b3648e20e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71e1b678-9125-4a5f-9cac-8e65e857eeac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 替换训练数据\n",
    "from fog_test.for_different_strength import mix_dataset\n",
    "\n",
    "'''\n",
    "接下来训练0.6强度的baseline。我们的增量训练是基于不加雾的baseline的，所以0.6强度的baseline不需要计算SI。\n",
    "\n",
    "'''\n",
    "val_fogged_strength = 1.2\n",
    "# 替换验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n",
    "origin_ratio = {\n",
    "    '1.2':1,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "574e5f02-83e2-4db7-9b70-80584cd85858",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=yolov5s.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_1.2, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/574f13c674b84b9c8a6b9a5abb27edd4\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val... 1048 images, 0 backg\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_1.2/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_1.2\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.65G    0.09098    0.05575    0.03718        128        640: 1\n",
      "tensor([2.01433], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.727     0.0789     0.0702     0.0287\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.65G    0.07444    0.04892    0.02537        133        640: 1\n",
      "tensor([1.95358], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.628       0.11     0.0863     0.0379\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.65G    0.07121    0.04905      0.024        131        640: 1\n",
      "tensor([1.96761], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.468      0.086     0.0757     0.0351\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.65G    0.06793    0.04924    0.02262        108        640: 1\n",
      "tensor([1.62605], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.486      0.174      0.153     0.0733\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.65G    0.06446    0.04827    0.02123        156        640: 1\n",
      "tensor([1.61653], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.276      0.131      0.124     0.0651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.65G    0.06207    0.04733    0.02012        123        640: 1\n",
      "tensor([1.48483], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.473      0.182      0.185     0.0938\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.65G    0.06059    0.04666    0.01925        174        640: 1\n",
      "tensor([1.77039], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.676      0.138      0.157     0.0819\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.65G    0.05943    0.04564    0.01824        166        640: 1\n",
      "tensor([1.76296], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.515      0.206       0.21      0.106\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.65G    0.05806    0.04588    0.01723        152        640: 1\n",
      "tensor([1.54873], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.573      0.242      0.254       0.13\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.65G    0.05778    0.04567    0.01682        136        640: 1\n",
      "tensor([1.43396], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.555      0.286      0.292      0.148\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.65G    0.05667    0.04537    0.01582        134        640: 1\n",
      "tensor([1.43059], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.616       0.29      0.309      0.161\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.65G    0.05596    0.04455    0.01551        182        640: 1\n",
      "tensor([1.46171], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.501      0.301      0.315      0.164\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.65G    0.05542    0.04483    0.01496        128        640: 1\n",
      "tensor([1.36140], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.472      0.336      0.327      0.165\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.65G     0.0551    0.04403    0.01435        112        640: 1\n",
      "tensor([1.46404], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.526      0.347      0.346      0.175\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.65G    0.05438    0.04419    0.01388        151        640: 1\n",
      "tensor([1.35628], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.499      0.323      0.318      0.159\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.65G    0.05443    0.04438    0.01366        132        640: 1\n",
      "tensor([1.44734], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.589       0.33      0.345      0.177\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.65G    0.05336    0.04342    0.01321        131        640: 1\n",
      "tensor([1.40220], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.658      0.319      0.357      0.192\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.65G     0.0535    0.04352    0.01275        159        640: 1\n",
      "tensor([1.44143], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.73      0.306      0.374      0.187\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.65G    0.05284     0.0431    0.01223        125        640: 1\n",
      "tensor([1.33815], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.599      0.351      0.387      0.206\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.65G    0.05265    0.04355    0.01217         88        640: 1\n",
      "tensor([1.19111], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.617      0.374      0.406      0.196\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.65G    0.05203    0.04225    0.01161        137        640: 1\n",
      "tensor([1.62227], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.639      0.332      0.393      0.202\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.65G    0.05158    0.04307      0.011        166        640: 1\n",
      "tensor([1.39507], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.646      0.363      0.396      0.206\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.65G    0.05127    0.04241    0.01092        161        640: 1\n",
      "tensor([1.41733], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.654      0.375      0.439      0.219\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.65G    0.05121    0.04199     0.0106        118        640: 1\n",
      "tensor([1.31738], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.632       0.38      0.428      0.222\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.65G    0.05063    0.04205    0.01036        151        640: 1\n",
      "tensor([1.49157], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.727      0.344      0.438      0.217\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.65G    0.05057    0.04224    0.01014        133        640: 1\n",
      "tensor([1.28428], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.646      0.414      0.476      0.242\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.65G    0.05006    0.04173   0.009861        154        640: 1\n",
      "tensor([1.49786], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.63      0.397      0.445      0.233\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.65G    0.05009     0.0419   0.009723        122        640: 1\n",
      "tensor([1.20674], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.662      0.396      0.455      0.232\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.65G    0.04963    0.04163   0.009273        123        640: 1\n",
      "tensor([1.13593], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.61      0.393      0.439      0.223\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.65G    0.04928    0.04109   0.009181        127        640: 1\n",
      "tensor([1.30757], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.641      0.415      0.453      0.233\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.65G     0.0493    0.04014    0.00912        127        640: 1\n",
      "tensor([1.16210], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.665      0.412       0.49      0.244\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.65G    0.04882     0.0407    0.00877        122        640: 1\n",
      "tensor([1.27257], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.699      0.438      0.509       0.27\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.65G    0.04859    0.04089    0.00866        146        640: 1\n",
      "tensor([1.38402], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.659      0.441      0.498      0.259\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.65G    0.04849     0.0402   0.008456        202        640: 1\n",
      "tensor([1.50243], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.738      0.429      0.504      0.264\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.65G    0.04832    0.04022   0.008376         94        640: 1\n",
      "tensor([0.99707], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.743      0.414      0.508      0.267\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.65G    0.04805    0.04016   0.008207        152        640: 1\n",
      "tensor([1.34052], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.73      0.432      0.511      0.268\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.65G    0.04779    0.04004   0.008187        123        640: 1\n",
      "tensor([1.15220], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.735      0.413      0.476      0.241\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.65G     0.0477    0.04007   0.008153        162        640: 1\n",
      "tensor([1.16785], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.735       0.46      0.537      0.277\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.65G    0.04752    0.04013   0.007831        161        640: 1\n",
      "tensor([1.26171], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.726      0.422      0.486      0.259\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.65G    0.04718    0.03994   0.007536        122        640: 1\n",
      "tensor([1.11184], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.765      0.425      0.517      0.277\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.65G    0.04723    0.03952   0.007627        126        640: 1\n",
      "tensor([1.07878], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.696      0.442      0.516      0.269\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.65G    0.04656    0.03944   0.007321         90        640: 1\n",
      "tensor([1.07300], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.712      0.462      0.533       0.28\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.65G    0.04639    0.03928   0.007229        118        640: 1\n",
      "tensor([1.26910], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.714      0.444      0.514      0.274\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.65G    0.04642    0.03933   0.006941        157        640: 1\n",
      "tensor([1.26462], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.753       0.46       0.55       0.29\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.65G    0.04625    0.03923   0.006691        104        640: 1\n",
      "tensor([1.03617], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.757      0.478      0.561      0.288\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.65G    0.04602    0.03956   0.006733        157        640: 1\n",
      "tensor([1.14275], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.802      0.478      0.565      0.301\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.65G    0.04575    0.03869    0.00655        108        640: 1\n",
      "tensor([1.00830], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.77      0.475      0.569      0.299\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.65G    0.04561    0.03864    0.00672        159        640: 1\n",
      "tensor([1.19644], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.679      0.501      0.559      0.292\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.65G     0.0455    0.03871   0.006432        118        640: 1\n",
      "tensor([1.18393], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.72      0.498      0.557      0.293\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.65G    0.04512    0.03905   0.006333        176        640: 1\n",
      "tensor([1.33382], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.739      0.471      0.548      0.296\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.65G    0.04504     0.0383   0.006458        130        640: 1\n",
      "tensor([1.13425], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.794      0.447      0.564      0.302\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.65G    0.04463     0.0388   0.006173        178        640: 1\n",
      "tensor([1.35260], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.71      0.498      0.562      0.292\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.65G    0.04459    0.03813   0.006239        148        640: 1\n",
      "tensor([1.06698], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.769      0.496       0.58      0.303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.65G    0.04454    0.03787   0.006051        115        640: 1\n",
      "tensor([1.09950], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.763      0.463      0.572      0.294\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.65G    0.04422    0.03761   0.005888        124        640: 1\n",
      "tensor([1.09184], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.746      0.484      0.581      0.313\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.65G    0.04409    0.03747   0.006058        163        640: 1\n",
      "tensor([1.10851], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.697      0.508      0.574      0.305\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.65G    0.04377    0.03775   0.005766        200        640: 1\n",
      "tensor([1.26409], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.754      0.493      0.585      0.305\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.65G    0.04383    0.03793   0.005844        242        640:  ^C\n",
      "      57/99      3.65G    0.04383    0.03793   0.005844        242        640:  \n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "100epoch 的0.6\n",
    "不需要计算SI。\n",
    "'''\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--name fog_1.2 \\\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b0ac7aa-1daa-42fc-8e97-9735400ccf26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 本来只打算0, 0.6, 1.2的，但是肉眼观察和数据来看1.2的强度有点离谱了什么都看不见了已经，遂换0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be6faee9-a31e-4ea4-b237-0dcef475f827",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3fbf19-d635-4060-a405-f658b7d1344b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a334a58-e68c-4d88-a33a-b9fdf9fa8d66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fe5d7af-5c0e-4cbb-adf2-349dc97522b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 替换训练数据\n",
    "from fog_test.for_different_strength import mix_dataset\n",
    "\n",
    "'''\n",
    "接下来训练0.6强度的baseline。我们的增量训练是基于不加雾的baseline的，所以0.6强度的baseline不需要计算SI。\n",
    "\n",
    "'''\n",
    "val_fogged_strength = 0.9\n",
    "# 替换验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n",
    "origin_ratio = {\n",
    "    '0.9':1,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6cbde45-c353-48e9-b844-6b599164dbf2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=yolov5s.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0.9, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/a7fa91bca32d4775b6fd664f9b7a5544\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0.92/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0.92\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.71G    0.08133    0.04844    0.03337        128        640: 1\n",
      "tensor([1.80681], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.733      0.123      0.119     0.0413\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.71G     0.0665    0.04149    0.02466        124        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda information\n",
      "       1/99      3.71G    0.06513    0.04071    0.02269        133        640: 1\n",
      "tensor([1.65916], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.309      0.323       0.21     0.0917\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.71G    0.05987    0.03962    0.01979        131        640: 1\n",
      "tensor([1.52034], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.458      0.219      0.185     0.0858\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.71G    0.05409    0.03879    0.01694        108        640: 1\n",
      "tensor([1.25718], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.635      0.393      0.382      0.174\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.71G     0.0495    0.03736    0.01478        156        640: 1\n",
      "tensor([1.25998], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.639      0.251       0.28      0.138\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.71G    0.04691    0.03681    0.01368        123        640: 1\n",
      "tensor([1.11230], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.727      0.389      0.466      0.245\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.71G      0.045    0.03587    0.01215        174        640: 1\n",
      "tensor([1.34226], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.638      0.492      0.528      0.277\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.71G    0.04393    0.03503    0.01122        166        640: 1\n",
      "tensor([1.39953], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.716      0.488      0.533      0.287\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.71G    0.04283    0.03507    0.01015        152        640: 1\n",
      "tensor([1.13178], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.644      0.447      0.486       0.26\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.71G    0.04203     0.0347   0.009666        136        640: 1\n",
      "tensor([1.08687], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.741      0.527      0.607      0.344\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.71G    0.04135    0.03434   0.008742        134        640: 1\n",
      "tensor([1.09591], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.699      0.641      0.687      0.384\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.71G    0.04049    0.03346   0.008271        182        640: 1\n",
      "tensor([1.13836], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.758      0.632      0.701      0.393\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.71G    0.04003    0.03375   0.007665        128        640: 1\n",
      "tensor([0.93566], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.795      0.624      0.706      0.408\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.71G    0.03893    0.03283   0.007212        112        640: 1\n",
      "tensor([1.02573], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.8      0.647      0.723      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.71G    0.03857    0.03288   0.006918        151        640: 1\n",
      "tensor([0.96034], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.74       0.66      0.713      0.423\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.71G    0.03822    0.03287   0.006712        132        640: 1\n",
      "tensor([0.96266], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.818      0.689      0.759      0.459\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.71G    0.03759    0.03211   0.006341        131        640: 1\n",
      "tensor([0.91107], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.664       0.77      0.458\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.71G    0.03759    0.03209    0.00623        159        640: 1\n",
      "tensor([1.06067], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.781      0.708      0.768      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.71G    0.03682    0.03158   0.005877        125        640: 1\n",
      "tensor([0.85583], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.693      0.777       0.46\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.71G    0.03656    0.03179   0.005845         88        640: 1\n",
      "tensor([0.75621], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.861      0.695      0.778      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.71G    0.03569    0.03067   0.005572        137        640: 1\n",
      "tensor([1.12908], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.778      0.689      0.757      0.462\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.71G    0.03579    0.03138   0.005475        166        640: 1\n",
      "tensor([0.97324], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.851      0.697      0.779      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.71G    0.03544    0.03075   0.005213        161        640: 1\n",
      "tensor([0.97951], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.722      0.802      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.71G    0.03445    0.03021    0.00499        151        640: 1\n",
      "tensor([0.93381], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.854      0.742      0.812      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.71G    0.03432    0.03032    0.00481        133        640: 1\n",
      "tensor([0.87104], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.834      0.743      0.806      0.502\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.71G    0.03433    0.03014   0.004721        154        640: 1\n",
      "tensor([1.02317], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.847      0.756      0.827      0.514\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.71G    0.03395    0.03007   0.004743        122        640: 1\n",
      "tensor([0.81111], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868       0.71      0.808      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.71G     0.0337    0.02981   0.004429        123        640: 1\n",
      "tensor([0.74816], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.736      0.836      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.71G    0.03374    0.02941   0.004407        127        640: 1\n",
      "tensor([0.79871], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.741      0.828      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.71G    0.03315    0.02839   0.004322        127        640: 1\n",
      "tensor([0.81670], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.751      0.823      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.71G    0.03311    0.02899   0.004294        122        640: 1\n",
      "tensor([0.86856], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.761      0.848      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.71G    0.03287    0.02909   0.004161        146        640: 1\n",
      "tensor([0.88841], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.738      0.838      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.71G    0.03242    0.02849   0.004099        202        640: 1\n",
      "tensor([0.97111], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.854      0.752      0.835      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.71G    0.03252    0.02835   0.003983         94        640: 1\n",
      "tensor([0.72861], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.788      0.852       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.71G    0.03242    0.02844   0.004004        152        640: 1\n",
      "tensor([0.91157], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.832      0.801      0.847      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.71G    0.03215    0.02819   0.003941        123        640: 1\n",
      "tensor([0.73833], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89       0.79      0.856      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.71G    0.03205    0.02827   0.004067        162        640: 1\n",
      "tensor([0.83660], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.769      0.847      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.71G    0.03169    0.02834   0.003908        161        640: 1\n",
      "tensor([0.87152], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.775      0.858      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.71G     0.0314    0.02817   0.003764        122        640: 1\n",
      "tensor([0.75470], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.752       0.85      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.71G    0.03152    0.02773   0.003864        126        640: 1\n",
      "tensor([0.71538], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.785      0.854      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.71G    0.03111    0.02766    0.00371         90        640: 1\n",
      "tensor([0.69236], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.769      0.863      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.71G    0.03088    0.02749   0.003569        118        640: 1\n",
      "tensor([0.81375], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.794      0.862      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.71G    0.03085    0.02745   0.003439        157        640: 1\n",
      "tensor([0.83097], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.796      0.857      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.71G    0.03088    0.02741   0.003395        104        640: 1\n",
      "tensor([0.58504], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.768      0.864      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.71G    0.03076    0.02777   0.003354        157        640: 1\n",
      "tensor([0.82495], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.797      0.866       0.58\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.71G    0.03068    0.02702   0.003342        108        640: 1\n",
      "tensor([0.62664], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.797      0.862      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.71G    0.03032    0.02688   0.003382        159        640: 1\n",
      "tensor([0.83406], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.801      0.866      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.71G    0.02992    0.02668   0.003285        118        640: 1\n",
      "tensor([0.74489], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.796      0.865      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.71G    0.02995    0.02708   0.003233        176        640: 1\n",
      "tensor([0.93174], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.786      0.862      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.71G    0.02991    0.02669   0.003333        130        640: 1\n",
      "tensor([0.72682], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.786      0.868      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.71G    0.02976    0.02683   0.003162        178        640: 1\n",
      "tensor([0.90607], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.787      0.874      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.71G    0.02956    0.02641   0.003175        148        640: 1\n",
      "tensor([0.75169], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.828      0.887      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.71G    0.02943    0.02626   0.003101        115        640: 1\n",
      "tensor([0.71528], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.787      0.874      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.71G    0.02922    0.02609   0.003069        124        640: 1\n",
      "tensor([0.67922], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.794      0.886       0.61\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.71G      0.029    0.02575   0.003154        163        640: 1\n",
      "tensor([0.77289], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.781      0.875      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.71G    0.02905    0.02616   0.003064        200        640: 1\n",
      "tensor([0.82033], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.805      0.874      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.71G    0.02884    0.02598   0.003047        141        640: 1\n",
      "tensor([0.74914], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.791      0.869      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.71G    0.02862    0.02588   0.003154        146        640: 1\n",
      "tensor([0.74443], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.815      0.881      0.606\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.71G    0.02865    0.02575   0.002987        168        640: 1\n",
      "tensor([0.76645], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.783      0.868      0.605\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.71G    0.02863    0.02552   0.002872        175        640: 1\n",
      "tensor([0.79563], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.802      0.878      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.71G    0.02803    0.02558   0.002968        139        640: 1\n",
      "tensor([0.79665], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.781      0.868      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.71G    0.02801    0.02475   0.002864        117        640: 1\n",
      "tensor([0.65395], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.794      0.876       0.61\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.71G    0.02821    0.02517   0.002722        129        640: 1\n",
      "tensor([0.72079], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.817      0.887      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.71G    0.02795    0.02524   0.002897        109        640: 1\n",
      "tensor([0.65032], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.816      0.877      0.602\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.71G    0.02777    0.02512   0.002755        154        640: 1\n",
      "tensor([0.79846], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.824      0.885      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.71G    0.02756      0.025   0.002739        119        640: 1\n",
      "tensor([0.68606], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.823      0.883      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.71G    0.02754    0.02448   0.002784        153        640: 1\n",
      "tensor([0.76022], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.849      0.893      0.609\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.71G    0.02742     0.0249   0.002694        116        640: 1\n",
      "tensor([0.67199], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.807      0.886      0.624\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.71G    0.02704    0.02418   0.002762        141        640: 1\n",
      "tensor([0.74294], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9       0.82      0.881      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.71G    0.02714    0.02446   0.002625        175        640: 1\n",
      "tensor([0.90646], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.839       0.89      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.71G    0.02723    0.02455   0.002708        161        640: 1\n",
      "tensor([0.74454], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.843      0.892      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.71G    0.02699    0.02415   0.002494        114        640: 1\n",
      "tensor([0.63860], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.811      0.887       0.63\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.71G    0.02703    0.02456   0.002656        141        640: 1\n",
      "tensor([0.72735], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.816      0.884       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.71G    0.02689    0.02446   0.002572        133        640: 1\n",
      "tensor([0.60887], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.835      0.895      0.631\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.71G    0.02653    0.02388   0.002502        159        640: 1\n",
      "tensor([0.78032], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.841      0.889      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.71G     0.0267    0.02388    0.00257        122        640: 1\n",
      "tensor([0.61981], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.829      0.892      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.71G    0.02652    0.02409     0.0025        137        640: 1\n",
      "tensor([0.69342], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.842      0.893      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.71G    0.02656    0.02368   0.002552        137        640: 1\n",
      "tensor([0.67880], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.817      0.889      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.71G    0.02602    0.02326   0.002541        161        640: 1\n",
      "tensor([0.76010], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.845      0.892      0.638\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.71G    0.02616     0.0237   0.002433        154        640: 1\n",
      "tensor([0.65991], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929       0.81       0.89      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.71G      0.026    0.02338   0.002426        181        640: 1\n",
      "tensor([0.78103], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.806      0.887      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.71G    0.02587    0.02325   0.002369        149        640: 1\n",
      "tensor([0.67321], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.813      0.887      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.71G    0.02581    0.02338   0.002301        118        640: 1\n",
      "tensor([0.63590], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.818      0.888      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.71G    0.02557    0.02308   0.002357        178        640: 1\n",
      "tensor([0.79431], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.812       0.89      0.642\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.71G    0.02557    0.02315   0.002321        140        640: 1\n",
      "tensor([0.69346], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.825      0.892      0.646\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.71G    0.02563    0.02318   0.002379        119        640: 1\n",
      "tensor([0.56449], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.817      0.886      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.71G    0.02549    0.02322   0.002248        114        640: 1\n",
      "tensor([0.54019], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.819      0.894      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.71G    0.02561    0.02292   0.002346        117        640: 1\n",
      "tensor([0.57962], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916       0.82      0.889      0.643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.71G    0.02541    0.02304   0.002262        118        640: 1\n",
      "tensor([0.58269], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9       0.83      0.889      0.645\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.71G    0.02527    0.02265   0.002151        115        640: 1\n",
      "tensor([0.61523], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.806      0.893      0.647\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.71G    0.02507    0.02239   0.002199        159        640: 1\n",
      "tensor([0.69930], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.832      0.894      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.71G    0.02502    0.02265   0.002254        165        640: 1\n",
      "tensor([0.71484], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.823      0.891      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.71G    0.02489     0.0223    0.00225        126        640: 1\n",
      "tensor([0.60088], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.839      0.894      0.653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.71G    0.02484     0.0223    0.00228        112        640: 1\n",
      "tensor([0.59764], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.839      0.896      0.655\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.71G    0.02462    0.02215   0.002083        121        640: 1\n",
      "tensor([0.58771], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.831      0.891      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.71G     0.0249    0.02212   0.002225        195        640: 1\n",
      "tensor([0.66803], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.842      0.894      0.655\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.71G    0.02483    0.02215   0.002152        101        640: 1\n",
      "tensor([0.57712], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.826      0.894      0.651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.71G    0.02455    0.02203   0.002167        137        640: 1\n",
      "tensor([0.55318], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.822      0.893      0.653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.71G    0.02454    0.02219   0.002132        115        640: 1\n",
      "tensor([0.52099], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924       0.83      0.892      0.654\n",
      "\n",
      "100 epochs completed in 0.966 hours.\n",
      "Optimizer stripped from runs/train/fog_0.92/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_0.92/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_0.92/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.839      0.896      0.655\n",
      "                   Car       1048       4012      0.944      0.917      0.965      0.773\n",
      "                   Van       1048        431      0.954      0.919      0.962       0.78\n",
      "                 Truck       1048        166      0.945      0.952      0.977      0.786\n",
      "                  Tram       1048         56      0.909      0.946      0.965       0.73\n",
      "            Pedestrian       1048        618      0.888      0.706      0.818      0.449\n",
      "        Person_sitting       1048         20      0.933      0.693      0.738      0.497\n",
      "               Cyclist       1048        234       0.89      0.793      0.879      0.593\n",
      "                  Misc       1048        138      0.865       0.79      0.863      0.632\n",
      "Results saved to \u001b[1mruns/train/fog_0.92\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_0.9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/a7fa91bca32d4775b6fd664f9b7a5544\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9300613700710045\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 218.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9653065407358515\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7732761016927255\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9440295717228512\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9165004985044866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3677.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8386572619425104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 23.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.878723469333391\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.593443837245751\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8897358870269086\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.793124949987695\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 186.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8258406758774085\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 17.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8625780035742776\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.631889139949942\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8652617908914205\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.7898550724637681\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 109.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7864342157819056\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 55.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.8183738888156131\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4493647897510365\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8880128925868395\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.7057090464250658\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 436.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7952241735886579\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7377651817716335\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.49715862446148973\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9326745664583502\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6930829478126775\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 14.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9272129750087902\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 5.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.96531645674875\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7298530034439412\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.9087621305712854\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9464285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9483280154162229\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 9.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.976552007989028\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7864196847720215\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9448741449469723\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9518072289156626\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 158.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9362217884404775\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 19.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9621096430766405\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7802446343398588\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9542213820632496\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9188886798631578\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 396.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.542923092842102, 3.8022398948669434)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.11883971827790027, 0.8960253613276314)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.04125424552052689, 0.654836849337936)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.30881784619974995, 0.9321764624521441)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.12309804002232783, 0.8494557601363188)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.024537062272429466, 0.08132734894752502)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0020826568361371756, 0.03337162360548973)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.022034985944628716, 0.048440683633089066)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.02541987970471382, 0.06831696629524231)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0026280602905899286, 0.025015996769070625)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.03923838213086128, 0.07342621684074402)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_0.9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/a7fa91bca32d4775b6fd664f9b7a5544\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_0.92\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : yolov5s.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.82 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 2 file(s), remaining 72.34 KB/451.39 KB\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "100epoch 的0.9\n",
    "由于将来可能要做从雾更浓忘雾不浓的反向增量，所以这个还是记录一下SI吧\n",
    "'''\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--name fog_0.9 \\\n",
    "--SI_enable \\\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "614c5305-2c53-4445-a152-265291994d6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b46ce2c8-60ab-4509-9a7f-e6a2308bcb5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 替换训练数据\n",
    "from fog_test.for_different_strength import mix_dataset\n",
    "\n",
    "'''\n",
    "接下来训练1.0强度的baseline。我们的增量训练是基于不加雾的baseline的，所以0.6强度的baseline不需要计算SI。\n",
    "\n",
    "'''\n",
    "val_fogged_strength = 1.0\n",
    "# 替换验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n",
    "origin_ratio = {\n",
    "    '1.0':1,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ff3120e-f25a-4e8f-bcd4-687e787d1d26",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=yolov5s.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_1.0, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/021eae49a1fd45a9b16fce106315f02e\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_1.0/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_1.0\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.71G     0.0821    0.04876    0.03369        128        640: 1\n",
      "tensor([1.83337], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.706      0.121     0.0953     0.0324\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.71G     0.0677    0.04363    0.02592        193        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "       1/99      3.71G    0.06564    0.04139     0.0231        133        640: 1\n",
      "tensor([1.66147], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.509      0.148      0.111     0.0518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.71G    0.06221     0.0406    0.02115        150        640:  fatal: unable to access 'https://github.com/ultralytics/yolov5/': Failed to connect to github.com port 443 after 130009 ms: Connection timed out\n",
      "       2/99      3.71G    0.06073    0.04053    0.02047        131        640: 1\n",
      "tensor([1.53401], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.447      0.223      0.175     0.0778\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.71G    0.05509    0.04033    0.01797        108        640: 1\n",
      "tensor([1.29659], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.562      0.274      0.249       0.11\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.71G    0.05099    0.03871    0.01574        156        640: 1\n",
      "tensor([1.24754], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.574      0.251      0.244      0.112\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.71G    0.04823    0.03798    0.01454        123        640: 1\n",
      "tensor([1.21425], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.675      0.387      0.451      0.237\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.71G    0.04632    0.03702    0.01324        174        640: 1\n",
      "tensor([1.33637], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.607      0.355      0.392      0.203\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.71G    0.04527    0.03614    0.01229        166        640: 1\n",
      "tensor([1.44671], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.502      0.425      0.439      0.231\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.71G     0.0442     0.0362     0.0112        152        640: 1\n",
      "tensor([1.14802], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.731       0.49       0.55      0.286\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.71G    0.04307    0.03568    0.01058        136        640: 1\n",
      "tensor([1.10058], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.641      0.527      0.542      0.294\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.71G    0.04244    0.03557   0.009889        134        640: 1\n",
      "tensor([1.10554], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.754      0.518      0.592      0.316\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.71G    0.04177    0.03467   0.009354        182        640: 1\n",
      "tensor([1.08457], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.695      0.542      0.585      0.326\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.71G    0.04118    0.03479   0.008612        128        640: 1\n",
      "tensor([0.95917], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.735      0.586      0.651      0.367\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.71G    0.04041    0.03382   0.008071        112        640: 1\n",
      "tensor([1.02650], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.75      0.601       0.65      0.369\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.71G    0.03973    0.03395   0.007814        151        640: 1\n",
      "tensor([1.02747], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.801      0.612      0.702      0.388\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.71G    0.03931    0.03377   0.007421        132        640: 1\n",
      "tensor([1.02009], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.767      0.637      0.697      0.392\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.71G    0.03883    0.03303   0.006968        131        640: 1\n",
      "tensor([0.90530], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.792      0.655      0.731      0.416\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.71G    0.03802    0.03294   0.006862        159        640: 1\n",
      "tensor([1.10019], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.795      0.694      0.748      0.438\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.71G    0.03807    0.03247   0.006497        125        640: 1\n",
      "tensor([0.88556], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.802      0.646      0.733      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.71G    0.03783    0.03268   0.006362         88        640: 1\n",
      "tensor([0.79448], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.777      0.669      0.741      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.71G     0.0372    0.03161   0.006068        137        640: 1\n",
      "tensor([1.15935], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.657      0.743      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.71G    0.03675    0.03233   0.005876        166        640: 1\n",
      "tensor([1.08491], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.802      0.687      0.759      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.71G    0.03643    0.03168   0.005737        161        640: 1\n",
      "tensor([1.02400], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.691      0.778      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.71G    0.03635    0.03133   0.005595        118        640: 1\n",
      "tensor([0.88932], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.685      0.778      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.71G    0.03566    0.03119   0.005481        151        640: 1\n",
      "tensor([0.99480], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.819      0.704      0.791      0.489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.71G    0.03564    0.03125   0.005261        133        640: 1\n",
      "tensor([0.90005], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.822      0.707      0.779      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.71G    0.03525    0.03094   0.005113        154        640: 1\n",
      "tensor([1.06004], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.819      0.729      0.784      0.469\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.71G    0.03471    0.03088   0.005172        122        640: 1\n",
      "tensor([0.85027], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.692      0.797      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.71G    0.03463    0.03066   0.004845        123        640: 1\n",
      "tensor([0.74450], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.851      0.735      0.812      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.71G    0.03454    0.03028   0.004796        127        640: 1\n",
      "tensor([0.79061], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.816      0.744      0.806      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.71G    0.03431    0.02934   0.004765        127        640: 1\n",
      "tensor([0.83078], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.729      0.801      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.71G    0.03405    0.02988   0.004577        122        640: 1\n",
      "tensor([0.83679], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.724      0.825      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.71G    0.03399    0.03002   0.004424        146        640: 1\n",
      "tensor([0.95784], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.737      0.829      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.71G    0.03389    0.02936   0.004307        202        640: 1\n",
      "tensor([1.04121], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.724      0.802      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.71G    0.03344    0.02916   0.004309         94        640: 1\n",
      "tensor([0.74156], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.746      0.833       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.71G    0.03341    0.02919   0.004312        152        640: 1\n",
      "tensor([1.02145], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.759      0.839      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.71G    0.03314    0.02904   0.004284        123        640: 1\n",
      "tensor([0.74491], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.728      0.831      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      3.71G    0.03289    0.02908   0.004243        162        640: 1\n",
      "tensor([0.84236], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.756      0.837       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.71G    0.03296    0.02925   0.004141        161        640: 1\n",
      "tensor([0.92692], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856       0.76      0.835      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.71G    0.03274    0.02906   0.004057        122        640: 1\n",
      "tensor([0.74509], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.752      0.841      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.71G    0.03238    0.02855   0.004037        126        640: 1\n",
      "tensor([0.75202], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.744      0.831      0.533\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.71G    0.03208    0.02857   0.003951         90        640: 1\n",
      "tensor([0.71488], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.733      0.824      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.71G    0.03189    0.02832    0.00379        118        640: 1\n",
      "tensor([0.81349], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.765      0.842       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.71G    0.03178    0.02825   0.003569        157        640: 1\n",
      "tensor([0.87137], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.736      0.838      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.71G    0.03197    0.02827   0.003684        104        640: 1\n",
      "tensor([0.62432], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.793      0.851      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.71G    0.03151    0.02851   0.003589        157        640: 1\n",
      "tensor([0.83532], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.85      0.798      0.851      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.71G    0.03164    0.02783   0.003488        108        640: 1\n",
      "tensor([0.63905], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906       0.77      0.853      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      3.71G    0.03129    0.02775   0.003573        159        640: 1\n",
      "tensor([0.86238], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.766      0.845      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.71G    0.03093    0.02763   0.003459        118        640: 1\n",
      "tensor([0.76731], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.776      0.852      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.71G    0.03101    0.02799    0.00347        176        640: 1\n",
      "tensor([0.97695], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.773      0.853      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.71G    0.03112    0.02758   0.003477        130        640: 1\n",
      "tensor([0.76707], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.792      0.847      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.71G    0.03043    0.02758   0.003272        178        640: 1\n",
      "tensor([0.96824], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.773      0.851      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.71G    0.03038    0.02719   0.003347        148        640: 1\n",
      "tensor([0.75664], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.809       0.85       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.71G    0.03034    0.02703   0.003192        115        640: 1\n",
      "tensor([0.74359], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.789      0.859      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.71G    0.03015    0.02684   0.003287        124        640: 1\n",
      "tensor([0.72904], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.763      0.856      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.71G    0.02991    0.02657   0.003278        163        640: 1\n",
      "tensor([0.78902], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.775      0.848      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.71G    0.02996    0.02697   0.003231        200        640: 1\n",
      "tensor([0.88842], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.803      0.858      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.71G    0.02973    0.02684    0.00316        141        640: 1\n",
      "tensor([0.74891], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.763      0.847      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.71G    0.02961    0.02668    0.00324        146        640: 1\n",
      "tensor([0.75883], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.819      0.872      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.71G    0.02966    0.02661    0.00313        168        640: 1\n",
      "tensor([0.77078], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.788      0.858      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.71G    0.02936    0.02621   0.002959        175        640: 1\n",
      "tensor([0.82874], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.786      0.864       0.59\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.71G    0.02898    0.02633   0.003063        139        640: 1\n",
      "tensor([0.79471], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.779       0.86      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.71G    0.02894    0.02549   0.002996        117        640: 1\n",
      "tensor([0.68471], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.781      0.868       0.59\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.71G    0.02912    0.02594   0.002848        129        640: 1\n",
      "tensor([0.71479], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.799      0.862      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.71G    0.02878    0.02596   0.003053        109        640: 1\n",
      "tensor([0.66366], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.778      0.854      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.71G    0.02885    0.02603   0.002858        154        640: 1\n",
      "tensor([0.83249], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.787      0.867      0.594\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.71G    0.02852    0.02582   0.002892        119        640: 1\n",
      "tensor([0.71914], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.785       0.87      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.71G    0.02831    0.02513   0.002942        153        640: 1\n",
      "tensor([0.77358], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.787      0.866      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.71G    0.02837    0.02579   0.002909        116        640: 1\n",
      "tensor([0.67678], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.825      0.878      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.71G    0.02803    0.02502   0.002885        141        640: 1\n",
      "tensor([0.81484], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924       0.81      0.876      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.71G    0.02805    0.02524   0.002783        175        640: 1\n",
      "tensor([0.90663], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.784      0.877      0.602\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.71G    0.02813    0.02533     0.0028        161        640: 1\n",
      "tensor([0.82148], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.812      0.875      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.71G    0.02786    0.02491   0.002687        114        640: 1\n",
      "tensor([0.67556], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.795      0.869      0.606\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.71G    0.02801     0.0254   0.002759        141        640: 1\n",
      "tensor([0.74790], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.783      0.873      0.607\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.71G    0.02779    0.02523   0.002709        133        640: 1\n",
      "tensor([0.63776], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.798      0.877      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.71G    0.02747    0.02473    0.00266        159        640: 1\n",
      "tensor([0.80646], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.804      0.869      0.604\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.71G    0.02762    0.02467   0.002705        122        640: 1\n",
      "tensor([0.64446], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.933      0.792      0.876      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.71G    0.02754    0.02499   0.002617        137        640: 1\n",
      "tensor([0.73389], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.798      0.871      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.71G    0.02741    0.02447    0.00261        137        640: 1\n",
      "tensor([0.69619], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.806       0.87      0.613\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.71G    0.02689    0.02405   0.002645        161        640: 1\n",
      "tensor([0.80496], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.917      0.793      0.868      0.608\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.71G    0.02708     0.0245   0.002571        154        640: 1\n",
      "tensor([0.68454], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897      0.813      0.875      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.71G    0.02689    0.02419   0.002546        181        640: 1\n",
      "tensor([0.80999], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.793      0.872      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.71G    0.02675    0.02403   0.002477        149        640: 1\n",
      "tensor([0.68661], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.808      0.875      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.71G    0.02673    0.02409   0.002423        118        640: 1\n",
      "tensor([0.66664], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.806      0.878      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.71G    0.02653    0.02392   0.002427        178        640: 1\n",
      "tensor([0.79791], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.795      0.872      0.617\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.71G    0.02643    0.02388   0.002417        140        640: 1\n",
      "tensor([0.71368], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.788      0.868      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.71G    0.02647    0.02398   0.002484        119        640: 1\n",
      "tensor([0.61562], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.906      0.798      0.874       0.62\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.71G    0.02638    0.02408   0.002375        114        640: 1\n",
      "tensor([0.55390], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.802      0.883      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.71G    0.02645    0.02369   0.002407        117        640: 1\n",
      "tensor([0.60542], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.813      0.883      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.71G     0.0263    0.02387   0.002389        118        640: 1\n",
      "tensor([0.59283], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.804      0.883      0.625\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.71G    0.02614    0.02345   0.002298        115        640: 1\n",
      "tensor([0.62793], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.821      0.886      0.634\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.71G    0.02589    0.02314   0.002296        159        640: 1\n",
      "tensor([0.79822], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.821      0.881      0.623\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.71G    0.02595    0.02345   0.002361        165        640: 1\n",
      "tensor([0.72633], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.941      0.801      0.882      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.71G    0.02578    0.02307   0.002354        126        640: 1\n",
      "tensor([0.60547], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.805      0.879      0.621\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.71G    0.02569    0.02307   0.002337        112        640: 1\n",
      "tensor([0.64277], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.799      0.881      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.71G    0.02545    0.02291   0.002172        121        640: 1\n",
      "tensor([0.61554], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.915      0.808      0.877      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.71G    0.02574    0.02279   0.002323        195        640: 1\n",
      "tensor([0.67420], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93       0.81      0.879      0.627\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.71G    0.02568    0.02293   0.002245        101        640: 1\n",
      "tensor([0.65893], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.807      0.878      0.626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.71G    0.02541    0.02283   0.002257        137        640: 1\n",
      "tensor([0.60566], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.813      0.879      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.71G    0.02538    0.02293   0.002191        115        640: 1\n",
      "tensor([0.55128], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.813      0.879      0.631\n",
      "\n",
      "100 epochs completed in 0.987 hours.\n",
      "Optimizer stripped from runs/train/fog_1.0/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/fog_1.0/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/fog_1.0/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.821      0.886      0.634\n",
      "                   Car       1048       4012      0.937      0.912      0.961      0.761\n",
      "                   Van       1048        431      0.923      0.919      0.962      0.767\n",
      "                 Truck       1048        166      0.927      0.915      0.963      0.752\n",
      "                  Tram       1048         56      0.897      0.946      0.937      0.712\n",
      "            Pedestrian       1048        618       0.86      0.695      0.798      0.438\n",
      "        Person_sitting       1048         20          1      0.612      0.759      0.498\n",
      "               Cyclist       1048        234      0.878      0.774      0.861      0.554\n",
      "                  Misc       1048        138      0.863      0.797      0.848      0.594\n",
      "Results saved to \u001b[1mruns/train/fog_1.0\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : fog_1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/021eae49a1fd45a9b16fce106315f02e\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9240450032374394\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 247.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9610876017526014\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7606899332767728\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9366606164728231\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.9117647058823529\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3658.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.8223466235856076\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 25.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8611555522893579\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5535822474081338\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8777729408504232\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.7735042735042735\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 181.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.8288445169614092\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 17.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8478855337871418\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.5942920033152752\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8632206579118489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.7971014492753623\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 110.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7689517867271702\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 70.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.7983211760129381\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.4379653000388196\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8599289269229149\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.6953829696548143\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 430.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7596863846361228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.75877380184827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.497814796052212\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6124954005388787\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.921063187502968\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 6.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9373860896023979\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.7119006550210609\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.8970219581435632\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9464285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9206131490917874\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.962916357428752\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7522285281332997\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9267470919767389\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.9145598708444687\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 152.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9207083489010307\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 33.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9618772316779985\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7667832831649479\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9226311923986343\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.9187935034802784\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 396.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5814934968948364, 3.80749249458313)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.09530222645091946, 0.8861935365536255)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.032405283456393486, 0.6335640300414743)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.44718901004594536, 0.9412929559243315)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.12081106796717248, 0.8247589420291614)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.0253768227994442, 0.08209917694330215)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0021718984935432673, 0.0336930938065052)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.022793442010879517, 0.0487586073577404)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.026159832254052162, 0.06931328773498535)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0030137035064399242, 0.026601340621709824)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.04008200764656067, 0.07587548345327377)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : fog_1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/021eae49a1fd45a9b16fce106315f02e\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/fog_1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : yolov5s.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.84 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 6 file(s), remaining 573.93 KB/1.51 MB\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 2 asset(s), remaining 11.21 KB/525.61 KB, Throughput 37.44 KB/s, ETA ~1s\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "100epoch 的1.0\n",
    "0.9和0.6过于相似，没有太大区别所以再训个1.0的。\n",
    "由于将来可能要做从雾更浓忘雾不浓的反向增量，所以这个还是记录一下SI吧\n",
    "'''\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--name fog_1.0 \\\n",
    "--SI_enable \\\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ee6ddf4-2a58-4916-9a5b-9648999a912d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dc0a598-e44f-4a1e-afde-5b402a052ac4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eca19de1-49ed-4996-83fe-04a11f238f52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1c18f2b-1e66-4ce8-b074-cd7bddbad85e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6625690b-3ee6-4f60-8dbd-525d365949a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "#这个是使用ewc的增量训练在没有加雾的第一个数据集上的效果\n",
    "\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/testing/image_2/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f7cbad8-984e-4a1a-941a-52158b30cfd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_02/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198       0.91      0.844      0.898      0.654\n",
      "                   Car       2244       8711      0.948      0.919      0.967      0.787\n",
      "                   Van       2244        861      0.945        0.9      0.944      0.756\n",
      "                 Truck       2244        333      0.949      0.961      0.968       0.81\n",
      "                  Tram       2244        138      0.926      0.971      0.974      0.744\n",
      "            Pedestrian       2244       1286      0.882      0.739      0.827      0.474\n",
      "        Person_sitting       2244         89      0.772      0.573      0.667      0.386\n",
      "               Cyclist       2244        496      0.927      0.819      0.906      0.591\n",
      "                  Misc       2244        284      0.932      0.873      0.929      0.682\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 1.0ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp30\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "开始测baseline\n",
    "首先是基于无雾数据集的测试集\n",
    "'''\n",
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_02/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "378f2846-4a58-48bd-9a98-92e3b18324e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0.6/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.875       0.71      0.808      0.531\n",
      "                   Car       2244       8711      0.908      0.857      0.924      0.698\n",
      "                   Van       2244        861      0.862      0.715      0.817      0.596\n",
      "                 Truck       2244        333      0.938      0.871      0.926      0.696\n",
      "                  Tram       2244        138      0.956      0.785       0.91      0.597\n",
      "            Pedestrian       2244       1286       0.83      0.644      0.738      0.386\n",
      "        Person_sitting       2244         89      0.667      0.652      0.696      0.363\n",
      "               Cyclist       2244        496      0.922      0.521      0.713      0.419\n",
      "                  Misc       2244        284      0.919      0.638      0.739      0.497\n",
      "Speed: 0.0ms pre-process, 0.7ms inference, 1.0ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp48\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是0.6雾训练集\n",
    "model = f'runs/train/fog_0.6/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f7af5f7-e97b-43fe-a19a-a8bb1fa83ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0.92/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.789       0.45      0.536      0.321\n",
      "                   Car       2244       8711      0.902       0.64      0.779      0.527\n",
      "                   Van       2244        861      0.606       0.46      0.497      0.334\n",
      "                 Truck       2244        333      0.899      0.417      0.595      0.434\n",
      "                  Tram       2244        138          1      0.254      0.389      0.211\n",
      "            Pedestrian       2244       1286      0.712      0.564      0.605      0.316\n",
      "        Person_sitting       2244         89      0.449      0.607       0.55      0.259\n",
      "               Cyclist       2244        496      0.935      0.288      0.406      0.227\n",
      "                  Misc       2244        284      0.809      0.373      0.465      0.263\n",
      "Speed: 0.1ms pre-process, 0.8ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp32\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是0.9雾训练集\n",
    "model = f'runs/train/fog_0.92/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d62d69e-c681-4c92-bcb3-092db2eacf2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_1.0/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198       0.76      0.364      0.439       0.26\n",
      "                   Car       2244       8711      0.886      0.539      0.704      0.462\n",
      "                   Van       2244        861      0.615      0.324      0.375      0.252\n",
      "                 Truck       2244        333      0.819      0.345      0.439      0.306\n",
      "                  Tram       2244        138      0.918      0.174       0.25      0.136\n",
      "            Pedestrian       2244       1286      0.715      0.516      0.576      0.292\n",
      "        Person_sitting       2244         89      0.473      0.449      0.468      0.247\n",
      "               Cyclist       2244        496      0.892      0.284      0.358      0.199\n",
      "                  Misc       2244        284      0.763      0.282      0.341      0.188\n",
      "Speed: 0.0ms pre-process, 1.0ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp49\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是1.0雾训练集\n",
    "model = f'runs/train/fog_1.0/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "878bfeca-c188-42dc-9212-fe0decc81684",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "216e2547-d96c-4154-b317-72f471b6d884",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c48d8373-4a77-4ab3-9e4e-1faf5ede96d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24aad770-c52b-4abb-b48a-da5cfda7f42b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d8f6bd0-a1f8-4fc7-b7f8-1c20b557a289",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a1af963-2514-4b4f-b774-6e6cb5d978ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61c8ef77-2feb-48d9-9917-fc20971ec770",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 然后是0.6雾测试集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/fogged_strength0.6/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84675d06-9e2d-49a6-94bf-73c9ee980d1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_02/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.743      0.492      0.564      0.344\n",
      "                   Car       2244       8711       0.87      0.673      0.804      0.543\n",
      "                   Van       2244        861      0.708      0.555      0.643      0.424\n",
      "                 Truck       2244        333      0.865      0.583      0.667      0.434\n",
      "                  Tram       2244        138      0.589       0.63      0.646      0.364\n",
      "            Pedestrian       2244       1286      0.704      0.487      0.573      0.311\n",
      "        Person_sitting       2244         89      0.798      0.178      0.233      0.108\n",
      "               Cyclist       2244        496      0.748      0.476       0.55      0.318\n",
      "                  Misc       2244        284      0.666      0.352      0.394      0.252\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp33\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_02/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "89c8204e-04fa-4efa-bf78-ac9c02958d0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0.6/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.923      0.844      0.907      0.653\n",
      "                   Car       2244       8711      0.954      0.914      0.967      0.782\n",
      "                   Van       2244        861      0.954      0.898      0.946      0.752\n",
      "                 Truck       2244        333      0.971      0.946      0.969      0.811\n",
      "                  Tram       2244        138      0.919      0.949      0.969      0.743\n",
      "            Pedestrian       2244       1286      0.894      0.718      0.822      0.469\n",
      "        Person_sitting       2244         89      0.843      0.665      0.769      0.404\n",
      "               Cyclist       2244        496      0.923      0.808      0.896      0.587\n",
      "                  Misc       2244        284      0.924      0.852      0.922      0.679\n",
      "Speed: 0.1ms pre-process, 1.1ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp34\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是0.6雾训练集\n",
    "model = f'runs/train/fog_0.6/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9fef7952-333d-4045-abf3-719b1e9e1701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0.92/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.916      0.804      0.875        0.6\n",
      "                   Car       2244       8711      0.942      0.896      0.955      0.743\n",
      "                   Van       2244        861       0.91      0.856      0.913        0.7\n",
      "                 Truck       2244        333      0.969      0.931      0.956      0.762\n",
      "                  Tram       2244        138      0.969      0.903      0.951      0.665\n",
      "            Pedestrian       2244       1286      0.874      0.703       0.79      0.436\n",
      "        Person_sitting       2244         89       0.77      0.639      0.733      0.391\n",
      "               Cyclist       2244        496      0.948       0.72      0.848      0.519\n",
      "                  Misc       2244        284      0.943      0.782      0.858      0.587\n",
      "Speed: 0.0ms pre-process, 0.8ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp35\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是0.9雾训练集\n",
    "model = f'runs/train/fog_0.92/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0f3b721-59e5-4938-a70b-590cea0e4b20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_1.0/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.851      0.656      0.751      0.472\n",
      "                   Car       2244       8711      0.898      0.818      0.902      0.651\n",
      "                   Van       2244        861      0.799      0.649      0.742      0.514\n",
      "                 Truck       2244        333      0.941      0.778      0.875      0.619\n",
      "                  Tram       2244        138      0.907      0.566      0.723      0.408\n",
      "            Pedestrian       2244       1286      0.819      0.648      0.724      0.384\n",
      "        Person_sitting       2244         89      0.601      0.596      0.638      0.346\n",
      "               Cyclist       2244        496      0.941      0.556      0.682      0.404\n",
      "                  Misc       2244        284      0.901      0.641      0.724      0.448\n",
      "Speed: 0.1ms pre-process, 1.0ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp50\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是1.0雾训练集\n",
    "model = f'runs/train/fog_1.0/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff9b927-b762-4198-8964-8813c3a36c9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0805097-b6b0-40e5-ab01-6615230dde23",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8393620c-92a2-4a80-b98f-b2e91faf5322",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be847270-e132-402e-a88d-47239641535e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 然后是0.9雾测试集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/fogged_strength0.9/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "570873ca-d108-4e0e-bd59-41f31ba3dd51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_02/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.751      0.208      0.283      0.172\n",
      "                   Car       2244       8711      0.876      0.432      0.645      0.402\n",
      "                   Van       2244        861      0.628      0.278      0.356      0.232\n",
      "                 Truck       2244        333      0.896       0.13      0.196      0.125\n",
      "                  Tram       2244        138      0.822     0.0942      0.133     0.0737\n",
      "            Pedestrian       2244       1286      0.789      0.299      0.417      0.232\n",
      "        Person_sitting       2244         89      0.844     0.0787     0.0933     0.0544\n",
      "               Cyclist       2244        496      0.702      0.204       0.26      0.156\n",
      "                  Misc       2244        284      0.449      0.144      0.164      0.105\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp36\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_02/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71d6b866-190a-4a54-9df4-7cf89b74f4ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0.6/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198        0.9      0.747      0.839      0.581\n",
      "                   Car       2244       8711      0.957      0.845      0.942      0.737\n",
      "                   Van       2244        861      0.929      0.763      0.867      0.651\n",
      "                 Truck       2244        333      0.973      0.871      0.916      0.737\n",
      "                  Tram       2244        138      0.916      0.826      0.914      0.658\n",
      "            Pedestrian       2244       1286      0.864      0.647      0.753      0.428\n",
      "        Person_sitting       2244         89      0.804      0.554      0.661      0.349\n",
      "               Cyclist       2244        496      0.886      0.692      0.806      0.499\n",
      "                  Misc       2244        284      0.872      0.775      0.851      0.589\n",
      "Speed: 0.0ms pre-process, 0.7ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp37\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是0.6雾训练集\n",
    "model = f'runs/train/fog_0.6/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ce76c47-2621-4321-9f75-ce060095f023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0.92/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.916      0.839      0.901      0.643\n",
      "                   Car       2244       8711       0.95      0.916      0.965      0.774\n",
      "                   Van       2244        861      0.946      0.895      0.937      0.739\n",
      "                 Truck       2244        333      0.973      0.961      0.973      0.805\n",
      "                  Tram       2244        138      0.924      0.942      0.969      0.725\n",
      "            Pedestrian       2244       1286       0.89       0.72      0.814      0.459\n",
      "        Person_sitting       2244         89      0.795       0.64      0.764      0.408\n",
      "               Cyclist       2244        496      0.916      0.792      0.881      0.572\n",
      "                  Misc       2244        284      0.936      0.845      0.905      0.662\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp38\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是0.9雾训练集\n",
    "model = f'runs/train/fog_0.92/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52e06adf-6816-4814-9585-3ee7c9124c2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_1.0/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.901      0.773      0.864      0.576\n",
      "                   Car       2244       8711      0.937      0.885      0.951      0.728\n",
      "                   Van       2244        861      0.918      0.811      0.896      0.654\n",
      "                 Truck       2244        333      0.965      0.919      0.958      0.736\n",
      "                  Tram       2244        138      0.885      0.884      0.941      0.601\n",
      "            Pedestrian       2244       1286      0.888      0.668      0.787      0.429\n",
      "        Person_sitting       2244         89      0.732      0.573      0.675      0.374\n",
      "               Cyclist       2244        496      0.938      0.712      0.835      0.517\n",
      "                  Misc       2244        284      0.946      0.732      0.868      0.571\n",
      "Speed: 0.1ms pre-process, 0.8ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp51\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是1.0雾训练集\n",
    "model = f'runs/train/fog_1.0/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# ewc强度0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ced5106-c17e-4fb7-a74f-58cba0149e1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d90c55b5-e8de-4c9c-aebb-e0564bd89b78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "269ebd7b-85d4-47ea-acdf-d1b1225ab550",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b16901c-72a9-4bc9-9d71-96cdf7119df4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd2c134c-9491-4204-9774-521e7b83c72f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71066a4d-ec02-46f3-bf18-193ee8500196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "# 然后是1.0雾测试集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/fogged_strength1.0/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2ffb835-38ff-4445-acb5-8e7028f6c145",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_02/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.708     0.0497      0.112      0.076\n",
      "                   Car       2244       8711      0.951      0.107      0.432      0.327\n",
      "                   Van       2244        861      0.432     0.0801     0.0922     0.0681\n",
      "                 Truck       2244        333      0.811      0.012     0.0329     0.0185\n",
      "                  Tram       2244        138          1          0     0.0057     0.0057\n",
      "            Pedestrian       2244       1286      0.865     0.0855      0.182     0.0934\n",
      "        Person_sitting       2244         89      0.682     0.0225     0.0301     0.0182\n",
      "               Cyclist       2244        496      0.797     0.0556     0.0938      0.056\n",
      "                  Misc       2244        284      0.127     0.0352     0.0284     0.0212\n",
      "Speed: 0.1ms pre-process, 0.7ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp55\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/fog_02/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1e1b6e9-2286-4ca3-914f-ec01410b9a03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0.6/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.639       0.16      0.194      0.128\n",
      "                   Car       2244       8711      0.865      0.269      0.416      0.312\n",
      "                   Van       2244        861      0.446       0.16      0.182      0.145\n",
      "                 Truck       2244        333       0.56     0.0766     0.0808     0.0623\n",
      "                  Tram       2244        138      0.712      0.058      0.068     0.0441\n",
      "            Pedestrian       2244       1286      0.757      0.255      0.333      0.194\n",
      "        Person_sitting       2244         89      0.732      0.157      0.166     0.0751\n",
      "               Cyclist       2244        496      0.723      0.163      0.188      0.116\n",
      "                  Misc       2244        284      0.317      0.144      0.117     0.0772\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 0.6ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp56\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是0.6雾训练集\n",
    "model = f'runs/train/fog_0.6/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e970a2f-9382-447a-a9d4-429d177b1e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_1.0/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 ba3ea0ef Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.814      0.532      0.629      0.371\n",
      "                   Car       2244       8711      0.912       0.69      0.819      0.569\n",
      "                   Van       2244        861       0.79      0.585      0.657      0.443\n",
      "                 Truck       2244        333      0.881      0.544      0.636       0.39\n",
      "                  Tram       2244        138      0.697      0.518      0.601       0.31\n",
      "            Pedestrian       2244       1286      0.814      0.541      0.638      0.335\n",
      "        Person_sitting       2244         89       0.77      0.416      0.519      0.265\n",
      "               Cyclist       2244        496       0.89      0.413      0.519      0.278\n",
      "                  Misc       2244        284      0.754      0.551      0.642      0.378\n",
      "Speed: 0.1ms pre-process, 1.0ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp57\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是1.0雾训练集\n",
    "model = f'runs/train/fog_1.0/weights/best.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
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   "execution_count": null,
   "id": "1ba3aae9-2bb4-4b0d-8c7b-f32b04cd4eb8",
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   "execution_count": null,
   "id": "fd851c8e-3a29-45c4-94d7-a59f9af89ee7",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "408b4dce-b77b-4dd0-b5b6-42590319b3aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2708f05b-1ed3-4bc9-814f-e48686a804d4",
   "metadata": {},
   "outputs": [],
   "source": []
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